Regarding your first point on investing in industries that covary vs. are causally related: you’re right that mission hedging can also work when there is just covariance.
I think the main benefit of investing in companies that cause the bad activity is that it will have have a tighter covariance than investing in companies that do not cause the bad activity and we can know this ex ante.
I do take your point that this is potentially more of a reputational risk in investing in companies that cause the bad activity (for some cases, for some people). I do not think the reputational risk argument applies much to either small investors or some investments such as investing in technology companies to hedge against AI risks.
Now, your last point I find most interesting: if the efficient market hypothesis (EHM) doesn’t hold then it’s better to invest in things that have a high covariance. I have a strong intuition that EHM holds for publically traded stocks, especially for small investors, who don’t make a big fuzz about investing.
Overall, I feel drawn to selecting investments that cause the bad activity due to higher certainty about high future covariance.
Now do our donations go further when the problems in the world get worse? I’m inclined to say “yes”, but I think it’s a very small effect.
Yes, this crucially depends on whether there are increasing returns to scale to charitable intervention, which is another assumption. However, for me the assumption has has intuitive appeal. I can imagine the effect size to be substantial in some cases (I now give a toy model in the beginning of the text). Think about the effect of public good type interventions where the cost-effectiveness scales pretty linearly with the problem (how many beings are affected).
I took a look at your calculation and I’m sorry to say that I don’t quite understand it. However, based on the numbers that I see, I think that plugging in different parameters into the model would also not be entirely unreasonable. But yes, I agree think it might be interesting to have more empirical validation on this.
I think our disagreement might boil down to different intuitions about whether EMH holds on the stock market and whether there returns to scale i.e. whether a charity becomes more effective as the problem gets bigger. I think this is somewhat likely in some cases (but I’m not completely confident in this). So I’m still pretty convinced about this to the point where I would advice people to seriously, though carefully consider using mission hedging over your covariance approach.
Also, I think that generalizing to selecting estimates based on covariance with charity value is the right framework to use here, instead of just looking at this sort of hedging.
I think investing in corporations that cause the bad activity is theoretically equivalent to this and in fact is based on finding a (distal) cause of charity effectiveness. However, as mentioned above it assumes increasing returns to scale.
But I just thought about finding a more proximal cause of charity effectiveness, that can still be directly implemented on the stock market and maybe this might be shorting the endowment of your favorite charity. Will Macaskill made a similar comment on your post saying that maybe it might be worth considering shorting FB if OpenPhil is still heavily reliant on it.
Maybe your favourite charity has an endowment and it itself doesn’t hedge against risks (because their portfolio is not optimally diversified).
okay let me explain the spreadsheet better. I was comparing investments in an irrelevant market, to investments in a relevant market. Each investment has a 1⁄3 chance of growing 0%, a 1⁄3 chance of growing 5%, and a 1⁄3 chance of growing 10%. The top spreadsheet shows the value of your money if you invest it in an irrelevant market. The bottom spreadsheet shows the value of your money if you invest it in a relevant market. For instance if you invest in a relevant market and the relevant market doesn’t change, then you get 0% on your investments and 0% change in donation value so your donations are worth 100% what they were worth before. If you invest in an irrelevant market, and both markets go up by 5%, then your donations would be worth 1 1.05 1.05 = 110.25 % if the covariance is 100%, but here the covariance is 40% so the calculation is 1 1.05 1.02 = 107.10%. Both numbers on the right are the average of the nine grid squares to the left, so they are the expected value of your investment after one year.
It’s really really simplistic math but I just tried to get a sense of the scale of the effect, it turned out to be small.
These are excellent comments, thank you!
Regarding your first point on investing in industries that covary vs. are causally related: you’re right that mission hedging can also work when there is just covariance. I think the main benefit of investing in companies that cause the bad activity is that it will have have a tighter covariance than investing in companies that do not cause the bad activity and we can know this ex ante. I do take your point that this is potentially more of a reputational risk in investing in companies that cause the bad activity (for some cases, for some people). I do not think the reputational risk argument applies much to either small investors or some investments such as investing in technology companies to hedge against AI risks. Now, your last point I find most interesting: if the efficient market hypothesis (EHM) doesn’t hold then it’s better to invest in things that have a high covariance. I have a strong intuition that EHM holds for publically traded stocks, especially for small investors, who don’t make a big fuzz about investing. Overall, I feel drawn to selecting investments that cause the bad activity due to higher certainty about high future covariance.
Yes, this crucially depends on whether there are increasing returns to scale to charitable intervention, which is another assumption. However, for me the assumption has has intuitive appeal. I can imagine the effect size to be substantial in some cases (I now give a toy model in the beginning of the text). Think about the effect of public good type interventions where the cost-effectiveness scales pretty linearly with the problem (how many beings are affected).
I took a look at your calculation and I’m sorry to say that I don’t quite understand it. However, based on the numbers that I see, I think that plugging in different parameters into the model would also not be entirely unreasonable. But yes, I agree think it might be interesting to have more empirical validation on this.
I think our disagreement might boil down to different intuitions about whether EMH holds on the stock market and whether there returns to scale i.e. whether a charity becomes more effective as the problem gets bigger. I think this is somewhat likely in some cases (but I’m not completely confident in this). So I’m still pretty convinced about this to the point where I would advice people to seriously, though carefully consider using mission hedging over your covariance approach.
I think investing in corporations that cause the bad activity is theoretically equivalent to this and in fact is based on finding a (distal) cause of charity effectiveness. However, as mentioned above it assumes increasing returns to scale.
But I just thought about finding a more proximal cause of charity effectiveness, that can still be directly implemented on the stock market and maybe this might be shorting the endowment of your favorite charity. Will Macaskill made a similar comment on your post saying that maybe it might be worth considering shorting FB if OpenPhil is still heavily reliant on it. Maybe your favourite charity has an endowment and it itself doesn’t hedge against risks (because their portfolio is not optimally diversified).
okay let me explain the spreadsheet better. I was comparing investments in an irrelevant market, to investments in a relevant market. Each investment has a 1⁄3 chance of growing 0%, a 1⁄3 chance of growing 5%, and a 1⁄3 chance of growing 10%. The top spreadsheet shows the value of your money if you invest it in an irrelevant market. The bottom spreadsheet shows the value of your money if you invest it in a relevant market. For instance if you invest in a relevant market and the relevant market doesn’t change, then you get 0% on your investments and 0% change in donation value so your donations are worth 100% what they were worth before. If you invest in an irrelevant market, and both markets go up by 5%, then your donations would be worth 1 1.05 1.05 = 110.25 % if the covariance is 100%, but here the covariance is 40% so the calculation is 1 1.05 1.02 = 107.10%. Both numbers on the right are the average of the nine grid squares to the left, so they are the expected value of your investment after one year.
It’s really really simplistic math but I just tried to get a sense of the scale of the effect, it turned out to be small.